The present disclosure relates to databases in general, and to monitoring and enhancing statistical measures in databases, in particular.
In statistics, the term type-I errors refers to false positive results, i.e., cases in which a conclusion deduced upon a finite dataset of samples is incorrect for the general population, or in other words situations in which the null hypothesis is wrongly rejected.
As the number of statistical tests executed on the same dataset increases, the probability that one or more of the drawn conclusions is wrong, increases as well. This phenomenon is sometimes referred to as the multiple hypotheses problem.
In many research fields, including for example biology, epidemiology, social studies or others, one or more communities of researchers conduct multiple researches on a common database. In such environments, neglecting the multiple hypothesis problem may amplify the occurrence of community-wise false positives.
Yet another problem relates to the monitoring and control of database owners or managers over the usage and tests performed upon the available data. Data owners and managers are not always aware of the type and scope of tests performed upon the data, and can thus be misled about the real usage of the data, or under-compensated.